A novel enhancement method for low illumination images based on microarray camera

It is difficult but important to get clear information from the low illumination images. In recent years the research of the low illumination image enhancement has become a hot topic in image processing and computer vision. The Retinex algorithm is one of the most popular methods in the field and uniform illumination is necessary to enhance low illumination image quality by using this algorithm. However, for the different areas of an image with contrast brightness differences, the illumination image is not smooth and causes halo artifacts so that it cannot retain the detail information of the original images. To solve the problem, we generalize the multi-scale Retinex algorithm and propose a new enhancement method for the low illumination images based on the microarray camera. The proposed method can well make up for the deficiency of imbalanced illumination and significantly inhibit the halo artifacts as well. Experimental results show that the proposed method can get better image enhancement effect compared to the multi-scale Retinex algorithm of a single image enhancement. Advantages of the method also include that it can significantly inhibit the halo artifacts and thus retain the details of the original images, it can improve the brightness and contrast of the image as well. The newly developed method in this paper has application potential to the images captured by pad and cell phone in the low illumination environment.

Multi-scale fusion residual encoder-decoder approach for low illumination image enhancement

The sensing light source of the line scan camera cannot be fully exposed in a low light environment due to the extremely small number of photons and high noise,which leads to a reduction in image quality.A multi-scale fusion residual encoder-decoder(FRED)was proposed to solve the problem.By directly learning the end-to-end mapping between light and dark images,FRED can enhance the image’s brightness with the details and colors of the original image fully restored.A residual block(RB)was added to the network structure to increase feature diversity and speed up network training.Moreover,the addition of a dense context feature aggregation module(DCFAM)made up for the deficiency of spatial information in the deep network by aggregating the context’s global multi-scale features.The experimental results show that the FRED is superior to most other algorithms in visual effect and quantitative evaluation of peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM).For the factor that FRED can restore the brightness of images while representing the edge and color of the image effectively,a satisfactory visual quality is obtained under the enhancement of low-light.

Low and non-uniform illumination color image enhancement using weighted guided image filtering

In the state of the art,grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination.As these methods are applied to each RGB channel independently,imbalanced inter-channel enhancements(color distortion)can often be observed in the resulting images.On the other hand,images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring,halos,and over-enhancement.To address these problems,an improved RGB color image enhancement method is proposed for images captured under nonuniform illumination or in poor visibility,based on weighted guided image filtering(WGIF).Unlike the conventional retinex algorithm and its variants,WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component;it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization.To limit color distortion,RGB images are first converted to HSI(hue,saturation,intensity)color space,where only the intensity channel is enhanced,before being converted back to RGB space by a linear color restoration algorithm.Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination,with better visual quality and objective evaluation scores than from comparator algorithms.It is also efficient due to use of a linear color restoration algorithm.