The process of image registration and fusion is used to obtain a resultant image with better characteristics. The concept of image registration and fusion can be used in various domains such as remote sensing, satellite imaging, medical imaging, etc. But, as the smallest of errors can have great consequences in medical images, applying these concepts in medical imaging will make a great difference. Aligning images spatially into one single framework is attained through image registration. There are various methods or techniques to implement image registration. Landmark registration, a semi-automatic registration method is used where images are spatially aligned by selecting control points from both the images and applying geometric transformation that helps in translation, rotation, scaling and shearing. During fusion process intermixing of traits from source images is performed using Discrete Wavelet Transform and Bicubic interpolation. The resultant image helps the doctors for better clinical diagnosis.

Medical Image Registration Using Landmark Registration Technique and Fusion

Retinex-Centered Contrast Enhancement Method for Histopathology Images with Weighted CLAHE

Changes in surface water are normally detected by extracting the water features from individual multispectral (MS) images. We present an approach based on pixel fusion of MS and panchromatic (PAN) bands of multitemporal Landsat images to detect the changes in surface water. Spatial frequency and the maximum amplitude selection-based fusion rule on multiresolution undecimated wavelet transform (UDWT) coefficients for extracting and injecting the spatial and spectral content from MS and PAN images is introduced. Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper (ETM+) of 2010 and 2000 images acquired from the US Geological Survey were fused to investigate the potential of the proposed spatial frequency-based UDWT in highlighting the changes. Various component substitution and multiresolution analysis-based fusion algorithms were also applied. It was observed that UDWT with spatial frequency effectively extracts the spectral information from MS images and the spatial information from PAN images. The performance was evaluated based on visual interpretation and spatial and spectral quality assessment metrics. A high correlation coefficient (0.9992), high contrast similarity (0.99937), high structural similarity index measure (0.9884), less relative average spectral error (23.6426), less root mean squared error (2.2256), relatively less blind referenceless image spatial quality evaluator, natural image quality evaluator, and perception-based image quality evaluator significantly support the proposed fusion technique in yielding the pansharpened image, making it more suitable for detecting the multitemporal changes in remote sensing applications.

Detecting changes in multitemporal multispectral Landsat images using spatial frequency-based undecimated wavelet transform fusion

The regular image fusion method based on scalar has the problem how to prioritize and proportionally enrich image details in multi-sensor network. Based on multiple sensors to fuse and manipulate patterns of computer vision is practical. A fusion (integration) rule, bit-depth conversion, and truncation (due to conflict of size) on the image information are studied. Through multi-sensor images, the fusion rule based on weighted priority is employed to restructure prescriptive details of a fused image. Investigational results confirm that the associated details between multiple images are possibly fused, the prescription is executed and finally, features are improved. Visualization for both spatial and frequency domains to support the image analysis is also presented.

https://beei.org/index.php/EEI/article/download/1792/2133

Evaluation of weighted fusion for scalar images in multi-sensor network

Image fusion is a combination of two different images into one image. Image integration has a huge application field, from medical to satellite imaging, as this approach plays an important role. There are various methods available for image merging, and in particular, there are two categories in which image merging is subdivided, modified, and integrated with the domain. A newly created image compilation would be more accurate and informed than the original image. This paper introduces a single image integration process called DWT discrete wavelet transform (DWT). We will be comparing various combinations such as MIN MIN and MAX MIN. The final picture is compared by multiple criteria, and the best approach is inferred in this article. Panchromatic satellite images are the data used for this paper. For these pictures, this algorithm needs to be used, as satellite pictures demand high spectral and spatial modification. Techniques to share photographs are included in this situation. The merged image would have both local and optical characteristics in a single image through image fusion. 

An Effective Algorithm of Remote Sensing Image Fusion Based on Discrete Wavelet Transform

The process of Image fusion can be defined as the process of combining multiple input images into a single composite image. Our aim is to create a single output image from the collection of input images which contains a better explanation of the view than the one provided by any of the individual input images. The fundamental problem of image fusion is one of determining the best procedure for combining the several input images. The review adopted in this paper is that combining various images with prior information is best handled within a statistical outline. The manuscript presented here is a representative collection of the latest advances in the field of Image Fusion (IF) which is offered and a range of methods that are causative to its growth are presented. It describes the spatial and transforms domain fusion techniques such as, Principal Component Analysis (PCA), Independent Component Analysis (ICA), Discrete Cosine Transform (DCT) and wavelet domain techniques and others.

Nivedita Jha

Papers

A review on various image fusion algorithms