A no-reference image enhancement quality metric and fusion technique

Image quality has always been an important aspect of the image processing field. Subjective quality is useful since images are a visual medium, but objective quality measures are needed because they are unbiased and can be used as parts of larger processing systems. Many image quality metrics exist that attempt to give an objective score to an image based on its likeness to a reference. These metrics work well if the reference is known and the test image is assumed to be a distorted version of the reference. However, in areas such as image enhancement, the reference image is generally worse than the test image and measuring likeness between the two is not a good indication of visual quality. A no-reference image enhancement quality metric is proposed in this paper that uses three factors to score images: lightness, contrast, and noise. It has been shown in literature that certain ideal ranges for lightness and contrast exist, and image enhancement techniques tend to push an image towards these. The metric gives each pixel in an image a score based on its neighborhood statistics. An image fusion technique is also proposed that fuses multiple enhanced images into one based on the local scores obtained from the no-reference metric. It is shown that this fused image scores higher using the no-reference metric and also has superior visual quality