Deepak Gambhir

Improved fuzzy transform based image compression and fuzzy median filter based its artifact reduction: pairFuzzy

During image compression, visually significant edges should be well preserved for human perception. Despite existence of many image compression standards, joint photographic experts group (JPEG) is the most popularly used standard for image compression. However at low bit rate, JPEG compressed images exhibit blocking artifacts that adversely affect the visual image quality. Thus, to produce a high visual quality image at low bit rate, pairFuzzy algorithm that is simple and more efficient as compared to JPEG alongwith the capability of reducing artifact is proposed. The proposed algorithm is carried out in three steps. First, an image is preprocessed using competitive fuzzy edge detection which efficiently detects the edge pixels contained in the image. Second, based on the edge information the image is compressed and decompressed using improved fuzzy transform. Third, the reconstructed image is postprocessed using fuzzy switched median filter for artifact reduction. The subjective as well as objective analysis alongwith the comparison to recent methods proves the superiority of proposed algorithm.

A Novel Fusion Rule for Medical Image Fusion in Complex Wavelet Transform Domain

Medical image fusion is being used at large by clinical professionals for improved diagnosis and treatment of diseases. The main aim of image fusion process is to combine complete information from all input images into a single fused image. Therefore, a novel fusion rule is proposed for fusing medical images based on Daubechies complex wavelet transform (DCxWT). Input images are first decomposed using DCxWT. The complex coefficients so obtained are then fused using normalized correlation based fusion rule. Finally, the fused image is obtained by inverse DCxWT with all combined complex coefficients. The performance of the proposed method has been evaluated and compared both visually and objectively with DCxWT based fusion methods using state-of art fusion rules as well as with existing fusion techniques. Experimental results and comparative study demonstrate that the proposed fusion technique generates better results than existing fusion rules as well as with other fusion techniques.

Fuzzy edge detector based blocking artifacts removal of DCT compressed images

DCT compressed digital images vulnerable to block like visible distortions at low bit rate. To overcome these block distortions, a new compressed image artifact removal method as cascade of fuzzy edge detector and fuzzy based substitution is proposed. In this scheme, Gaussian type fuzzy edge detector is used for each overlapped block of a DCT compressed image. The central pixel of compressed image block is to be considered under block edge boundary depending on fuzzy edge detector and its value is substituted with combination of mean, median values of its neighbors and S-shaped fuzzy membership function values. Presented experimental results illustrate the performance of proposed algorithm in terms of visual appearance and also in terms of quantified values as MSE, PSNR.

Edge and Fuzzy Transform Based Image Compression Algorithm: edgeFuzzy

Since edges contain symbolically important image information, their detection can be exploited for the development of an efficient image compression algorithm. This paper proposes an edge based image compression algorithm in fuzzy transform (F-transform) domain. Input image blocks are classified either as low intensity blocks, medium intensity blocks or a high intensity blocks depending on the edge image obtained using the Canny edge detection algorithm. Based on the intensity values, these blocks are compressed using F-transform. Huffman coding is then performed on compressed blocks to achieve reduced bit rate. Subjective and objective evaluations of the proposed algorithm have been made in comparisons with RFVQ, FTR, FEQ and JPEG. Results show that the proposed algorithm is an efficient image compression algorithm and also possesses low time complexity.

Wavelet based fusion of fuzzy enhanced images captured under improper illumination

To increase the application of images captured under improper illumination, preprocessing of underexposed and overexposed images before their fusion in wavelet domain is proposed. Since images contain some fuzziness in nature, hence both the underexposed and overexposed images are preprocessed using fuzzy enhancement method based on Gaussian intensification function. These enhanced images are then decomposed using Á-trous algorithm in wavelet domain where the Á-trous coefficients are fused using average based fusion rule. Finally, the inverse Á-trous algorithm reconstructs the fused image that has proper contrast and contains all the detail present in input images. Both subjective and objective assessment is done to evaluate the performance of the proposed algorithm. The fused image obtained by fusing fuzzy enhanced overexposed and underexposed images using Á-trous algorithm is better than those achieved by fusing images using DWT with fuzzy enhancement, DWT and averaging method.

Fuzzy edge detector based adaptive quantization image coding: FuzzAQC

The new design method of an fuzzy edge detector based adaptive quantization image coding (fuzzAQC) is proposed. In this design first the image is decomposed to blocks and each block is AQC compressed according to their fuzzy classification as either edge or smooth blocks. This classification depends on Entropy Optimized Histogram based fuzzy edge detector. The block is coded with Bit plane, Quantization step and Block min or Block Mean. The experimental results shows that the proposed fuzzAQC scheme is superior to conventional AQC and Intensity based AQC on objective measures like MSE PSNR along with visual quality.

Robust and Secret Data Transmission Over the Noisy Channel

We propose the secret and robust data transmission over the noisy channel. The secret data is encrypted and permutated using the permutation function, further encoded the data using the error detection and correction code. Experiments show the good quality of the stego-image and resistant against the various noise attacks (Like Salt and Pepper)

Channel noise reduction for compressed images using singular value decomposition

The paper is based on channel noise estimation and its reduction in compressed images using singular value decomposition. Image compression reduces irrelevant and redundant image data so that image is stored in lesser space and can be transmitted efficiently. Reducing the storage area increases the capacity of storage medium as well as the channel bandwidth. However, when the compressed image is transmitted, noise is added to it via transmission channel and the image is distorted. Therefore, accurate noise estimation and its reduction in a wide variety of vision and image processing applications is an important issue. An efficient algorithm has been developed which is based on the study of singular values of noise corrupted images and estimates the noise level in images that is further used for setting up a threshold for wavelet denoising. This dependency of threshold value on the esimation of noise level results in a better quality of denoised image. This algorithm has been applied to JPEG and JPEG2000 compressed images and corresponding results have been analyzed in terms of parameters like MSE and PSNR. The algorithm is more reliable, shows robust behavior over a visual content and noise conditions and it is more efficient as compared to the other relevant existing methods.

Waveatom transform-based multimodal medical image fusion

Medical image fusion produces a fused image that is extensively used by physicians for medical analysis and treatment. The fused image, so obtained, contains the complementary features present in different medical images obtained from imaging devices of single modality or of multiple modalities. The potential capabilities of waveatoms have been explored in many applications such as image denoising, fingerprint identification, compression; therefore, waveatom transform-based medical image fusion is proposed. The proposed fusion method is experimented on various sets of medical images and compared with recent state-of-the-art fusion methods. Results prove that the fused images obtained from the proposed method have better clarity and enhanced information and are practically more helpful for quick diagnosis and better treatment of diseases.

Fusion of Color Images Based on Fuzzy Transform and Spatial Frequency

Multiple images of a scene, captured using different imaging devices and containing complementary information, are required to be combined into a single fused image. The fused image, so obtained, should ideally contain all important features of individual input images. Further, color image processing is also gaining importance in most of the real-life applications. Color images provide better visual information and are highly suitable for human visual perception. Motivated by the gaining importance of color image processing, fusion of color images is proposed. The fusion algorithm is proposed in HSV color space where the luminance components of multiple input images, based on fuzzy transform and spatial frequency, are fused. The chrominance information of input images provides color to the fused image. The fused image contains all important information and possesses natural color appearance. Experiments performed on various pairs of visible–infrared and multifocus images demonstrate the superiority of the...