Dilbag Singh

Efficient Landsat image fusion using fuzzy and stationary discrete wavelet transform

ABSTRACT Image fusion is the concept to integrate multiple same scene images while drawing out maximum radiometric information from them by avoiding noise and fictional data. The main objective is to improve the radiometric quality of fused image compared to individual images of the same scene. Existing methods are found to be efficient, but if the similar radiometric information is fused into every image, it produces redundant high frequency of pixels. Therefore, to overcome this issue, in this paper a fuzzy and stationary discrete wavelet transform (FSDWT)-based image fusion technique is proposed. It decomposes Landsat image into stationary values, and then it preserves the radiometric data by using fuzzy if-then rules. In the last phase, FSDWT injects high-frequency blocks from input images and returns a single Landsat image with maximum radiometric data. Quantitative analysis has clearly demonstrated that FSDWT has better structural detail, spatial resolution and spectral information than existing methods.

Modified gain intervention filter based dehazing technique

Abstract Haze reduces the visibility of outdoor images. The majority of existing dehazing techniques perform poorly whenever objects are similar to airlight, and no shadow is cast on them. Also, existing approaches still suffer from the problems of colour distortion, edge preservation, and halo artefacts. To solve these issues, a modified gain coefficient filter based Dark channel prior (DCP) for single image dehazing is proposed. To reduce the colour distortion, restoration model of DCP is also redefined. The experimental results indicate that the proposed technique provides better results as compared with existing approaches.

Dehazing of remote sensing images using improved restoration model based dark channel prior

ABSTRACT Haze degrades visual information of remotely sensed images. Therefore, haze removal is a demanding and significant task for visual multispectral information improvement. The existing haze removal techniques utilize different restrictions and before restoring hazy images in an efficient manner. The review of existing haze removal methods demonstrates that the haze-free images suffer from colour distortion and halo artefacts problems. To solve these issues, an improved restoration model based dark channel prior is proposed in this paper. The proposed technique has redefined transmission map, with the aim to reduce the colour distortion problem. The modified joint trilateral filter is also utilized to improve the coarse estimated atmospheric veil. The experimental results reveal that the proposed approach provides visually significant haze-free images and also preserves the significant detail.

Comprehensive survey on haze removal techniques

Image haze removal techniques are extensively used in several outdoor applications. Lack of sufficient knowledge that is required to restore hazy images, the existing techniques usually use various attributes and assign constant values to these attributes. Unsuitable assignment to these attributes does not provide desired dehazing results. The primary objective of this review paper is to provide a structured outline of some well-known haze removal techniques. This paper also focuses on the methods which can assign optimal values to image dehazing attributes. The review has revealed that the meta-heuristic techniques can attain the optimistic haze removal parameters and also concurrently develops an optimistic objective function to estimate the depth map efficiently. Finally, this paper describes the various issues and challenges of image dehazing techniques, which are required to be further studied.

Single image haze removal using integrated dark and bright channel prior

In recent years, the dark channel prior (DCP) has been proven to be an adequate haze removal model. However, its procedure causes annoying halo and gradient reversal artifacts. To remove these issu...

Dehazing of remote sensing images using fourth-order partial differential equations based trilateral filter

Remote sensing images taken in hazy situations are degraded by scattering of atmospheric particles, which greatly influences the efficiency of visual systems. Therefore, the visibility restoration of hazy images becomes a significant area of research. In this study, a fourth-order partial differential equations based trilateral filter (FPDETF) dehazing approach is proposed to enhance the coarse estimated atmospheric veil. FPDETF is able to reduce halo and gradient reversal artefacts. It also preserves the radiometric information of haze-free images. The visibility restoration phase is also refined to reduce the colour distortion of dehazed images. The proposed technique has been evaluated on ten well-known remote sensing images and also compared with seven well-known existing dehazing approaches. The experimental results reveal that the proposed technique outperforms others in terms of contrast gain and percentage of saturated pixels.

Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring

Air pollutants such as benzene (

A novel dehazing model for remote sensing images

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