Image Fusion via Sparse Regularization with Non-Convex Penalties

The L1 norm regularized least squares method is often used for finding sparse approximate solutions and is widely used in 1-D signal restoration. Basis pursuit denoising (BPD) performs noise reduction in this way. However, the shortcoming of using L1 norm regularization is the underestimation of the true solution. Recently, a class of non-convex penalties have been proposed to improve this situation. This kind of penalty function is non-convex itself, but preserves the convexity property of the whole cost function. This approach has been confirmed to offer good performance in 1-D signal denoising. This paper demonstrates the aforementioned method to 2-D signals (images) and applies it to multisensor image fusion. The problem is posed as an inverse one and a corresponding cost function is judiciously designed to include two data attachment terms. The whole cost function is proved to be convex upon suitably choosing the non-convex penalty, so that the cost function minimization can be tackled by convex optimization approaches, which comprise simple computations. The performance of the proposed method is benchmarked against a number of state-of-the-art image fusion techniques and superior performance is demonstrated both visually and in terms of various assessment measures

Feature-Level Multi-focus Image Fusion using Neural Network and Image Enhancement

Image Processing applications have grown vastly in real world. Commonly due to limited depth of optical field lenses, it becomes inconceivable to obtain an image where all the objects are in focus. Image fusion deals with creating an image where all the objects are in focus. After image fusion, it plays an important role to perform other tasks of image processing such as image enhancement, image segmentation, and edge detection. This paper describes an application of Neural Network (NN), a novel feature-level multifocus image fusion technique has been implemented, which fuses multi-focus image using classification. The image is divided into blocks. The block feature vectors are fed to feed forward NN. The trained NN is then used to fuse any pair of multi-focus images. The implemented technique used in this paper is more efficient. The comparisons of the different existing approaches along with the implementing method by calculating different parameters like PSNR,RMSE

Fusion of Noisy Multi-sensor Imagery

Interest in fusing multiple sensor data for both military and civil applications has beengrowing. Some of the important applications integrate image information from multiple sensorsto aid in navigation guidance, object detection and recognition, medical diagnosis, datacompression, etc. While, human beings may visually inspect various images and integrateinformation, it is of interest to develop algorithms that can fuse various input imagery to producea composite image. Fusion of images from various sensor modalities is expected to produce anoutput that captures all the relevant information in the input. The standard multi-resolution-based edge fusion scheme has been reviewed in this paper. A theoretical framework is given forthis edge fusion method by showing how edge fusion can be framed as information maximisation.However, the presence of noise complicates the situation. The framework developed is used toshow that for noisy images, all edges no longer correspond to information. In this paper, varioustechniques have been presented for fusion of noisy multi-sensor images.  These techniques aredeveloped for a single resolution as well as using multi-resolution decomposition. Some of thetechniques are based on modifying edge maps by filtering images, while others depend onalternate definition of information maps. Both these approaches can also be combined.Experiments show that the proposed algorithms work well for various kinds of noisy multi-sensor images

Thermography data fusion and non-negative matrix factorization for the evaluation of cultural heritage objects and buildings

The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve nondestructive testing, medical analysis (computer aid diagnosis/detection—CAD), and arts and archeology, among many others. In the arts and archeology field, infrared technology provides significant contributions in terms of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard nonnegative matrix factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by nonnegative least squares active-set algorithm (SNMF2) and eigen-decomposition approaches such as principal component analysis (PCA) in thermography, and candid covariance-free incremental principal component analysis in thermography to obtain the thermal features. On the one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet-based data fusion combines the data of each method with PCA to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue, a fresco, a painting on canvas, and a building were analyzed using the above-mentioned methods, and the accuracy of defect (or targeted) region segmentation up to 71.98%, 57.10%, 49.27%, and 68.53% was obtained, respectively

Design and Testing of DWT based Image Fusion System using MATLAB -Simulink

ABSTRACT: Image fusion is extracting the required information from the two input images. The resultant will be the complete featured images. This is done by averaging the two images. in this paper focusing on DWT with different filters are haar, biorthogonal and daubechies to measure the quality of the image .When PSNR performance is high with MSE is low ,image quality is good. The DWT filters gives the different PSNR value, by comparing the filters, daubechies filter is chosen as best. This design is analysed and tested in MATLAB SIMULINK R2012b.The model is generated using MATLAB software