Enhancement of Very Low Light Images Using the YIQ Space Based on the CLAHE and Sigmoid Mapping with High Colour Restoration

Improving low-light images plays an important role in many image processing applications, the most important of which are night images, medical images [1, 2], scenes with irregular lighting, and images taken at high depths. Therefore, we sought to improve low-light images. The availability of technology and tools that simplify image alteration has increased the use of digital images in recent years. Users can manually regulate how much light enters the lens by manually altering the lens size whilst using conventional cameras. Digital cameras only need a basic understanding of the user and how they operate in the direction of light. Light should be transformed into electrical charges and the image into a series of zeros and ones to represent all coloured dots in the image [3]. Marine life and fish, breath-taking scenery and mystery shipwrecks are amongst the unique attractions that the environment offers. In addition to underwater images, underwater imaging has generated significant interest in numerous fields of technology and science research, including the examination of underwater cables and infrastructure, the detection of man-made objects and the operation of underwater vehicles, marine biology research and archaeology [4]. Until recently, image enhancement has been used in various fields of science and engineering, including computer vision, biomedicine, astrophotography and atmospheric sciences. Image improvement methods point to a convincing outcome because these techniques not only satisfy the need for increased outputs that are visually appealing but also bring back the consistency and sturdiness of outdoor viewing. Different techniques have been developed to lessen the distortion caused by the low lighting. With the growth of social media platforms, such as Facebook and YouTube, capturing photos or videos of people’s daily lives and posting them online have become common. One of the easiest methods for enhancing images, histogram equalisation (HE) [5], increases dynamic range but has issues with over-enhancement; many researchers have developed this technique [6, 7]. In the study, Zhou et al [8] introduced parallel nonlinear adaptive enhancement (PNAE) techniques to improve colour images with different lighting. Firstly, they used intensity transform via local neighbourhood depending on the intensity mapping function by Taylor transform, one of the disadvantages of this method is that it does not provide good contrast improvement at low lighting levels. This method enhanced the contrast, but not the areas with uniform lighting. Another important traditional method is contrast limited adaptive histogram equalisation (CLAHE) [9]. In this algorithm, the variance augmentation methods address the drawbacks of traditional graph equalisation. CLAHE was created initially for medical imaging; it has the ability to enhance low-contrast images, such as those from gateway films. Many studies have developed the CLAHE method in improving the image, such as the researches [10, 11]. In the study [12], Wang et al. suggested an optimisation approach using nature conservation objectively, where the lightness order error was measured. Secondly, the image is examined using a bright-pass filter to separate it into reflection and illumination, which define the details and character of the image, respectively. Thirdly, they suggested using a two-log transform to map illumination, thereby striking a balance between naturalness and detail.

Ancuti et al [4] suggested a workable method for restoring underwater images that had been damaged by scattering and weak absorption. Hardware or technical understanding of underwater conditions or scene structure is unnecessary for a single image phrase. It is based on combining two images made from a white-balanced copy of the original image’s degraded counterpart. The verification demonstrates that our technique enhances the accuracy of numerous image processing applications, including picture segmentation and keypoint matching; it is mostly independent of camera settings. In the study [13], the contrast enhancement approach (CEA), used to improve contrast for dark photographs with unpredictable lighting without harming the details in bright parts, was suggested by an algorithm. A solution that works with the ‘YCbCr’ model was suggested given that the luminance part ‘Y’ was used individually with this colour model. Then, the recently created sigmoid function was used to optimise the brightness ‘Y’ portion of the image, one of the weaknesses of this method is that it does not provide good color improvement at low lighting levels. Daway et al [3] suggested using logic methods to create a Fuzzy Logic-Based Sigmoidal Function (FLSMF) to boost the colour images’ contrast and lighting. The colour compounds remained unchanged when the FLBSMF algorithm was applied to the lighting component using only the YIQ colour space. The suggested technique has been contrasted with various algorithms, including fuzzy logic, the optimisation of which utilises a modification of the membership function based on quadratic operations, it is considered a good algorithm that improves lighting and contrast in a balanced manner. Ren et al [14] suggested the Retinex low-order modulator model (LR3M) approach, which is the first to pre-inject a low-order material into the Retinex breakdown process to suppress noise in the reflection map. This technique benefits from simple sectional illumination and successive suppression of reflection noise, where LR3M is used to improve image in low light, Despite the high color recovery in this method, it smooth the image, which reduces contrast and increases blur.

The proposed method produces better-quality noise reduction and optimisation results than more recent techniques for various pictures and movies. Singh et al [15] suggested a fresh reflection model and principal component analysis-based low-light image enhancement (LIME) technique. Based on the reflection model, the suggested algorithm operates adaptively with dark pictures. Then, to increase the overall contrast, a CLAHE finite contrast adaptive graph equalisation model is utilised in conjunction with an image brightness enhancement strategy based on the Fechner principle. The suggested strategy produces superior outcomes to previous methods for subjective and objective evaluations. Abraham et al [16] suggested an algorithm to enhance very low lightness images depending by using modified brightness based on sigmoid function (MBSF). This algorithm used brightness low lightness areas by YIQ colour space, including sigmoid mapping for lightness component.