Abstract
In this work, the authors have proposed a method for improving the visual quality of 2D color images suffering from low illumination. The input image is converted to HSV (Hue, Saturation, Value) color space, and the V component is subjected to high pass Laplace filter. The filtered output is then made to undergo a two-stage classifier and a brightness correction process. Finally, the resultant image obtained is gamma-corrected using an optimum gamma value computed using a well-known meta-heuristic based optimization technique namely, particle swarm optimization (PSO). The corrected V component is combined back with the H and S components to reconstruct the final result. The authors have tested this method on a number of 2D color images of natural scenes and the result is found to be satisfactory. Also, the experimental results are compared with similar methods in terms of subjective and objective metrics.
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Das, S., Roy, M., Mukhopadhyay, S. (2021). Correcting Low Illumination Images Using PSO-Based Gamma Correction and Image Classifying Method. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_36
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