Multi-focus image fusion based on sparse decomposition and background detection

Image Fusion Based on Combined Multi-scale Decomposition and Improved Sparse Representation

“…On one hand, the sparse representation method is applied in image fusion based on multi-scale transform which can combine the advantages of sparse representation and multi-scale decomposition. It can solve the problem of decomposition level as well as low contrast of transform base image fusion [1,2]. As a result, the proposed method can better preserve the information in the source images.…”

Multi-focus image fusion based on non-negative sparse representation and patch-level consistency rectification

“…As a result of their successful applications in many computer vision and image processing tasks, spare representation (SR) [3] as well as its variants have been introduced to multi-sensor image fusion, including multi-focus image fusion, in recent years [1,2,[4][5][6][7][8][9]. In these SR-based fusion methods, the traditional SR model [3] seems to be the most popular one used to achieve the sparse coding of the input image patches [10].…”

Multi-Focus Image Fusion using Non-Local Mean Filtering and Stationary Wavelet Transform

“…In this imaging field, image fusion is the best technique with the couple of input images [1] and combines them to generate best vision quality input image. The goal of image fusion [4] [8] [17] [21] is having more improving informative resultant images in terms of edges, sharpness, clearness etc. Fusions with two input images are based on multi-sensor, multi-focus [20], multi-modality and multi-temporal etc.…”

“…Fusions with two input images are based on multi-sensor, multi-focus [20], multi-modality and multi-temporal etc. Further the classification of image fusion is categorized into three segment levels [4] [7] with pixel i.e. low level, features i.e.…”