Rajlaxmi Chouhan

Enhancement of dark and low-contrast images using dynamic stochastic resonance

In this study, a dynamic stochastic resonance (DSR)-based technique in spatial domain has been proposed for the enhancement of dark- and low-contrast images. Stochastic resonance (SR) is a phenomenon in which the performance of a system (low-contrast image) can be improved by addition of noise. However, in the proposed work, the internal noise of an image has been utilised to produce a noise-induced transition of a dark image from a state of low contrast to that of high contrast. DSR is applied in an iterative fashion by correlating the bistable system parameters of a double-well potential with the intensity values of a low-contrast image. Optimum output is ensured by adaptive computation of performance metrics - relative contrast enhancement factor ( F ), perceptual quality measures and colour enhancement factor. When compared with the existing enhancement techniques such as adaptive histogram equalisation, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, edge-preserving multi-scale decomposition and automatic controls of popular imaging tools, the proposed technique gives significant performance in terms of contrast and colour enhancement as well as perceptual quality. Comparison with a spatial domain SR-based technique has also been illustrated.

Noise-induced contrast enhancement using stochastic resonance on singular values

In this paper, a dynamic stochastic resonance (DSR)-based technique in singular value domain for contrast enhancement of dark images has been presented. The internal noise due to the lack of illumination is utilized using a DSR iterative process to obtain enhancement in contrast, colorfulness as well as perceptual quality. DSR is a phenomenon that has been strategically induced and exploited and has been found to give remarkable response when applied on the singular values of a dark low-contrast image. When an image is represented as a summation of image layers comprising of eigen vectors and values, the singular values denote luminance information of each such image layer. By application of DSR on the singular values using the analogy of a bistable double-well potential model, each of the singular values is scaled to produce an image with enhanced contrast as well as visual quality. When compared with performance of some existing spatial domain enhancement techniques, the proposed DSR-SVD technique is found to give noteworthy better performance in terms of contrast enhancement factor, color enhancement factor and perceptual quality measure.

Internal noise-induced contrast enhancement of dark images

A contrast enhancement technique using scaling of internal noise of a dark image in discrete cosine transform (DCT) domain has been proposed in this paper. The mechanism of enhancement is attributed to noise-induced transition of DCT coefficients from a poor state to an enhanced state. This transition is effected by the internal noise present due to lack of sufficient illumination and can be modeled by a general bistable system exhibiting dynamic stochastic resonance. The proposed technique adopts a local adaptive processing and significantly enhances the image contrast and color information while ascertaining good perceptual quality. When compared with the existing enhancement techniques such as adaptive histogram equalization, gamma correction, single-scale retinex, multi-scale retinex, modified high-pass filtering, multi-contrast enhancement, multi-contrast enhancement with dynamic range compression, color enhancement by scaling, edge-preserving multi-scale decomposition and automatic controls of popular imaging tool, the proposed technique gives remarkable performance in terms of relative contrast enhancement, colorfulness and visual quality of enhanced image.

Noise-induced contrast enhancement of dark images using non-dynamic stochastic resonance

In this paper, a nonlinear non-dynamic stochastic resonance-based technique has been proposed for enhancement of dark and low contrast images. A low contrast image is treated as a subthreshold signal and noise-enhanced signal processing is applied to improve its contrast. The proposed technique uniquely utilizes addition of external noise to neutralize the effect of internal noise (due to insufficient illumination) of a low contrast image. Random noise is added repeatedly to an image and is successively hard-thresholded followed by overall averaging. By varying the noise intensities, noise induced resonance is obtained at a particular optimum noise intensity. Performance of the proposed technique has been investigated for four types of noise distributions - gaussian, uniform, poisson and gamma. Quantitative evaluation of their performances have been done in terms of contrast enhancement factor, color enhancement and perceptual quality measure. Comparison with other existing spatial domain techniques shows that the proposed technique gives remarkable enhancement while ascertaining good perceptual quality.

Wavelet-based contrast enhancement of dark images using dynamic stochastic resonance

In this paper, a dynamic stochastic resonance (DSR)-based technique has been proposed for contrast enhancement of dark and low contrast images in discrete wavelet transform (DWT) domain. Traditionally, the performance of a stochastic resonance (SR)-based system is improved by addition of external noise. However, in the proposed DSR-based approach, the internal noise of an image has been utilized for the purpose of contrast enhancement. The degradation due to inadequate illumination is treated as noise, and is used to produce a noise-induced transition of the image from a low-contrast state to a high-contrast state. Stochastic resonance is induced in the approximation and detail coefficients in an iterative fashion, producing an increase in variance and mean of the coefficient distribution. Optimal output response is ensured by selection of optimal of bistable system parameters. An iterative algorithm is followed to achieve target value of performance metrics, such as relative contrast enhancement factor (F), perceptual quality measures (PQM), and color enhancement factor (CEF), at minimum iteration count. When compared with the existing SR-based and non SR-based enhancement techniques in spatial and frequency domains, the proposed technique is found to give noteworthy performance in terms of contrast enhancement, perceptual quality, as well as colorfulness.

Contrast Enhancement of Dark Images using Stochastic Resonance in Wavelet Domain

In this paper, a dynamic stochastic resonance (DSR)-based technique in discrete wavelet transform (DWT) domain is presented for the enhancement of very dark grayscale and colored images. Generally in DSR, the performance of an input signal can be improved by addition of external noise. However in this paper, the intrinsic noise of an image has been utilized for the purpose of contrast enhancement. The DSR procedure iteratively tunes the DWT coefficients using bistable system parameters. The DSR-based technique significantly enhances the image without introducing any blocking, ringing or spot artifacts. The algorithm has been optimized and made adaptive. Performance of the given technique has been measured in terms of distribution separation measure (DSM), target-to- background enhancement measure based on standard deviation (TBEs) and target-to-background enhancement measure based on entropy (TBEe). When compared with the existing enhancement techniques such as histogram equalization, gamma correction, single-scale retinex, multi- scale retinex, modified high-pass filtering and Fourier-based DSR, the DWT-based DSR technique gives better performance in terms of visual information, color preservation and computational complexity of the enhancement process.

Robust watermark extraction using SVD-based dynamic stochastic resonance

In this paper, a novel dynamic stochastic resonance (DSR)-based non-blind watermark extraction technique has been proposed for robust extraction of a grayscale watermark. The watermark embedding has been carried out using singular value decomposition (SVD). Dynamic stochastic resonance has been strategically used to improve the robustness of the extraction algorithm by utilizing the noise added during attacks itself. Resilience of this technique to attacks has been tested in the presence of various noise, geometrical, enhancement, compression and filtering attacks. Using the DSR-based proposed extraction algorithm, a very robust extraction of watermark can be done without trading-off with visual quality of the watermarked image. Performance of the proposed technique has also been compared with the plain SVD-based and hybrid DCT-SVD based technique and is found to give better performance.

Dynamic stochastic resonance-based grayscale logo extraction in hybrid SVD-DCT domain

Abstract This paper presents a novel dynamic stochastic resonance (DSR)-based technique for robust extraction of a grayscale logo from a tampered watermarked image. The watermark embedding is done on the singular values (SV) of the discrete cosine transform (DCT) coefficients of the cover image. DSR is then strategically applied during the logo extraction process where the SV of DCT coefficients are tuned following a double-well potential model by utilizing the noise introduced during attacks. The resilience of this technique has been tested in the presence of various noises, geometrical distortions, enhancement, compression, filtering and watermarking attacks. The proposed DSR-based technique for logo extraction gives noteworthy robustness without any significant trade-off in perceptual transparency of the watermarked image. A maximization approach has been adopted for the selection of bistable double-well parameters to establish noise-enhanced resonance. When compared with existing watermark extraction techniques based in SVD, DCT, SVD-DCT domains, as well as with their combination with DSR, the results suggest that remarkable improvement of robustness is achieved by using DSR on singular values of DCT.

Image enhancement and dynamic range compression using novel intensity-specific stochastic resonance-based parametric image enhancement model

This paper presents a noise-aided image enhancement algorithm focussed on addressing images that have a large dynamic range, i.e., images with both dark and bright regions. The application of a new mathematical model, in a shifted double-well system exhibiting stochastic resonance, is investigated for such images. The new mathematical model addresses the shortcomings of earlier SR-based enhancement model by deriving parameters purely from input values (instead of input statistics). This model is specific to spatial domain pixel representation and operates on a revised iterative equation. This iterative processing is here applied selectively to the under-illuminated regions of the image, characterized as the De Vries-Rose (DVR) region of a human psychovisual model. The idea of suitably modifying the existing universal image quality index is also proposed for its participation in iteration termination, and to gauge the property of dynamic range compression. While the iterative algorithm is terminated using the revised image quality index, entropy maximization, and contrast quality of DVR region with constraints on perceptual quality, the performance of the proposed algorithm is also characterized by observing color enhancement and subjective scores on visual quality.

Dynamic stochastic resonance-based improved logo extraction in discrete cosine transform domain

Display Omitted Stochastic resonance based blind logo extraction in cosine transform domain has been presented.It improves the robustness of an algorithm by utilizing the degradation introduced during attacks.Iterative process tunes the coefficients of the possibly attacked watermarked image.The effect of noise can be suppressed and hidden information is enhanced. In this paper, a dynamic stochastic resonance (DSR) technique is used for blind watermark extraction in discrete cosine transform (DCT) domain. The watermark embedding has been done on mid-band DCT coefficients. DSR has been used to improve the robustness of the extraction algorithm by utilizing the degradation introduced during attacks. DSR is an iterative process that tunes the coefficients of the possibly attacked watermarked image so that the effect of noise is suppressed and hidden information is enhanced. Resilience of this technique has been tested in the presence of various attacks. An adaptive optimization procedure has been adopted for the selection of bistable parameters to achieve maximum correlation coefficient under minimum computational complexity. Using the proposed technique, robust extraction of watermark is obtained without trading-off with visual quality of the watermarked image. When compared with the plain DCT-based technique, DSR-based technique has been found to give remarkable performance.